Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today’s data-driven world, mastering data labeling is not just an advantage, it’s a necessity. Data Labeling in Machine Learning using Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.
With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, and Libra. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.
By the end of this book, you’ll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.

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Spis treści

Data Labeling in Machine Learning with Python. Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models eBook -- spis treści

• 1. Exploring Data for Machine Learning
• 2. Labeling Data for Classification
• 3. Labeling Data for Regression
• 4. Exploring Image Data
• 5. Labeling Image Data Using Rules
• 6. Labeling Image Data Using Data Augmentation
• 7. Labeling Text Data
• 8. Exploring Video Data
• 9. Labeling Video Data
• 10. Exploring Audio Data
• 11. Labeling Audio Data
• 12. Hands-On Exploring Data Labeling Tools